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• significance 15
• power 16
• simplicity 14
• R 51
• breakpoint regression 3
• multiple comparisons 5
• silly tests 6
• graphics 21
• effect sizes 11
• measurement error 8
• design features 11
• organisation 5
• logistic regression 7
• correlational studies 7
• mixed-effects models 11
• cluster-randomised experiments 6
• generalised additive models 3
• non-linearities 9
• missing data 1
• open science 3
• correction 1
• multiple regression 9
• reading 2
• tutorial 10
• machine learning 1
• random forests 1
• R tip 1
• bootstrapping 2
• replication 2
• predictive modelling 2
• assumptions 5
• research questions 4
• Bayesian statistics 7
• data entry 1
• preprint 2
• cannonball 2
• brms 3
• contrast coding 3
• walkthrough 1
• collinearity 1
• Stan 2
• reliability 2
• nonparametric tests 1
• research design 1
• teaching materials 1
• DAG 1

significance

• Capitalising on covariates in cluster-randomised experiments
• Nonparametric tests aren't a silver bullet when parametric assumptions are violated
• Adjusting for a covariate in cluster-randomised experiments
• Walkthrough: A significance test for a two-group comparison
• Confidence interval-based optional stopping
• The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
• On correcting for multiple comparisons: Five scenarios
• Experiments with intact groups: spurious significance with improperly weighted t-tests
• Analysing experiments with intact groups: the problem and an easy solution
• Power simulations for comparing independent correlations
• Explaining key concepts using permutation tests
• Assessing differences of significance
• A purely graphical explanation of p-values
• Calibrating p-values in 'flexible' piecewise regression models
• Analysing pretest/posttest data

power

• Capitalising on covariates in cluster-randomised experiments
• Nonparametric tests aren't a silver bullet when parametric assumptions are violated
• Adjusting for a covariate in cluster-randomised experiments
• Increasing power and precision using covariates
• Abandoning standardised effect sizes and opening up other roads to power
• On correcting for multiple comparisons: Five scenarios
• Causes and consequences of unequal sample sizes
• The problem with cutting up continuous variables and what to do when things aren't linear
• Analysing experiments with intact groups: the problem and an easy solution
• Covariate adjustment in logistic mixed models: Is it worth the effort?
• Covariate adjustment in logistic regression — and some counterintuitive findings
• Silly significance tests: Tests unrelated to the genuine research questions
• Power simulations for comparing independent correlations
• More on why I don't like standardised effect sizes
• The curious neglect of covariates in discussions of statistical power
• Analysing pretest/posttest data

simplicity

• In research, don't do things you don't see the point of
• Nonparametric tests aren't a silver bullet when parametric assumptions are violated
• Five suggestions for simplifying research reports
• Adjusting for a covariate in cluster-randomised experiments
• Before worrying about model assumptions, think about model relevance
• Common-language effect sizes
• Surviving the ANOVA onslaught
• Silly significance tests: The main effects no one is interested in
• Silly significance tests: Tests unrelated to the genuine research questions
• A more selective approach to reporting statistics
• Overaccuracy and false precision
• Silly significance tests: Tautological tests
• Silly significance tests: Balance tests
• Analysing pretest/posttest data

R

• An R function for computing Levenshtein distances between texts using the word as the unit of comparison
• The consequences of controlling for a post-treatment variable
• Capitalising on covariates in cluster-randomised experiments
• Tutorial: Visualising statistical uncertainty using model-based graphs
• Nonparametric tests aren't a silver bullet when parametric assumptions are violated
• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• Baby steps in Bayes: Incorporating reliability estimates in regression models
• Baby steps in Bayes: Accounting for measurement error on a control variable
• Drawing scatterplot matrices
• Adjusting for a covariate in cluster-randomised experiments
• Collinearity isn't a disease that needs curing
• Interactions in logistic regression models
• Walkthrough: A significance test for a two-group comparison
• Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
• Introducing cannonball - Tools for teaching statistics
• Looking for comments on a paper on model assumptions
• Checking model assumptions without getting paranoid
• Confidence interval-based optional stopping
• Creating comparable sets of stimuli
• Interactions between continuous variables
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• Confidence intervals for standardised mean differences
• Some illustrations of bootstrapping
• What data patterns can lie behind a correlation coefficient?
• Common-language effect sizes
• Tutorial: Drawing a dot plot
• R tip: Ordering factor levels more easily
• Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates
• Tutorial: Drawing a boxplot
• Tutorial: Drawing a line chart
• Tutorial: Drawing a scatterplot
• Why reported R² values are often too high
• Experiments with intact groups: spurious significance with improperly weighted t-tests
• Drawing a scatterplot with a non-linear trend line
• Causes and consequences of unequal sample sizes
• The problem with cutting up continuous variables and what to do when things aren't linear
• Analysing experiments with intact groups: the problem and an easy solution
• Covariate adjustment in logistic mixed models: Is it worth the effort?
• Covariate adjustment in logistic regression — and some counterintuitive findings
• Some tips on preparing your data for analysis
• Power simulations for comparing independent correlations
• Explaining key concepts using permutation tests
• Thinking about graphs
• Some alternatives to bar plots
• Assessing differences of significance
• Silly significance tests: Balance tests
• A purely graphical explanation of p-values
• Calibrating p-values in 'flexible' piecewise regression models
• Analysing pretest/posttest data

breakpoint regression

• Baby steps in Bayes: Piecewise regression with two breakpoints
• Baby steps in Bayes: Piecewise regression
• Calibrating p-values in 'flexible' piecewise regression models

multiple comparisons

• The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
• Why reported R² values are often too high
• On correcting for multiple comparisons: Five scenarios
• Silly significance tests: Tests unrelated to the genuine research questions
• Calibrating p-values in 'flexible' piecewise regression models

silly tests

• In research, don't do things you don't see the point of
• Five suggestions for simplifying research reports
• Silly significance tests: The main effects no one is interested in
• Silly significance tests: Tests unrelated to the genuine research questions
• Silly significance tests: Tautological tests
• Silly significance tests: Balance tests

graphics

• Tutorial: Visualising statistical uncertainty using model-based graphs
• Five suggestions for simplifying research reports
• Drawing scatterplot matrices
• Before worrying about model assumptions, think about model relevance
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
• A closer look at a classic study (Bailey et al. 1974)
• Looking for comments on a paper on model assumptions
• Checking model assumptions without getting paranoid
• Interactions between continuous variables
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• What data patterns can lie behind a correlation coefficient?
• Tutorial: Drawing a dot plot
• R tip: Ordering factor levels more easily
• Tutorial: Drawing a boxplot
• Tutorial: Drawing a line chart
• Tutorial: Drawing a scatterplot
• Drawing a scatterplot with a non-linear trend line
• A more selective approach to reporting statistics
• Thinking about graphs
• Some alternatives to bar plots

effect sizes

• Abandoning standardised effect sizes and opening up other roads to power
• Confidence intervals for standardised mean differences
• Which predictor is most important? Predictive utility vs. construct importance
• What data patterns can lie behind a correlation coefficient?
• Common-language effect sizes
• Why reported R² values are often too high
• Covariate adjustment in logistic mixed models: Is it worth the effort?
• Covariate adjustment in logistic regression — and some counterintuitive findings
• More on why I don't like standardised effect sizes
• A more selective approach to reporting statistics
• Why I don't like standardised effect sizes

measurement error

• Interpreting regression models: a reading list
• Baby steps in Bayes: Incorporating reliability estimates in regression models
• Baby steps in Bayes: Accounting for measurement error on a control variable
• Abandoning standardised effect sizes and opening up other roads to power
• Which predictor is most important? Predictive utility vs. construct importance
• Controlling for confounding variables in correlational research: Four caveats
• More on why I don't like standardised effect sizes
• Why I don't like standardised effect sizes

design features

• Capitalising on covariates in cluster-randomised experiments
• Consider generalisability
• Suggestions for more informative replication studies
• Increasing power and precision using covariates
• Confidence interval-based optional stopping
• Creating comparable sets of stimuli
• Abandoning standardised effect sizes and opening up other roads to power
• Experiments with intact groups: spurious significance with improperly weighted t-tests
• Causes and consequences of unequal sample sizes
• Analysing experiments with intact groups: the problem and an easy solution
• Explaining key concepts using permutation tests

organisation

• Interpreting regression models: a reading list
• Creating comparable sets of stimuli
• Automatise repetitive tasks
• The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
• Some tips on preparing your data for analysis

logistic regression

• Tutorial: Visualising statistical uncertainty using model-based graphs
• Interpreting regression models: a reading list
• Interactions in logistic regression models
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• Covariate adjustment in logistic mixed models: Is it worth the effort?
• Covariate adjustment in logistic regression — and some counterintuitive findings

correlational studies

• Interpreting regression models: a reading list
• Baby steps in Bayes: Incorporating reliability estimates in regression models
• Baby steps in Bayes: Accounting for measurement error on a control variable
• Drawing scatterplot matrices
• Which predictor is most important? Predictive utility vs. construct importance
• What data patterns can lie behind a correlation coefficient?
• Controlling for confounding variables in correlational research: Four caveats

mixed-effects models

• Tutorial: Visualising statistical uncertainty using model-based graphs
• Interpreting regression models: a reading list
• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• Adjusting for a covariate in cluster-randomised experiments
• Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
• Consider generalisability
• Suggestions for more informative replication studies
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• Covariate adjustment in logistic mixed models: Is it worth the effort?

cluster-randomised experiments

• Capitalising on covariates in cluster-randomised experiments
• Five suggestions for simplifying research reports
• Adjusting for a covariate in cluster-randomised experiments
• Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
• Experiments with intact groups: spurious significance with improperly weighted t-tests
• Analysing experiments with intact groups: the problem and an easy solution

generalised additive models

• Increasing power and precision using covariates
• Interactions between continuous variables
• The problem with cutting up continuous variables and what to do when things aren't linear

non-linearities

• Drawing scatterplot matrices
• Before worrying about model assumptions, think about model relevance
• Baby steps in Bayes: Piecewise regression with two breakpoints
• Baby steps in Bayes: Piecewise regression
• Increasing power and precision using covariates
• Interactions between continuous variables
• What data patterns can lie behind a correlation coefficient?
• Drawing a scatterplot with a non-linear trend line
• The problem with cutting up continuous variables and what to do when things aren't linear

missing data

• Causes and consequences of unequal sample sizes

open science

• Five suggestions for simplifying research reports
• The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
• Some advantages of sharing your data and code

correction

• Experiments with intact groups: spurious significance with improperly weighted t-tests

multiple regression

• The consequences of controlling for a post-treatment variable
• Tutorial: Visualising statistical uncertainty using model-based graphs
• Interpreting regression models: a reading list
• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• Drawing scatterplot matrices
• Collinearity isn't a disease that needs curing
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• Why reported R² values are often too high

reading

• A closer look at a classic study (Bailey et al. 1974)
• Surviving the ANOVA onslaught

tutorial

• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• Interactions in logistic regression models
• Looking for comments on a paper on model assumptions
• Checking model assumptions without getting paranoid
• Tutorial: Adding confidence bands to effect displays
• Tutorial: Plotting regression models
• Tutorial: Drawing a dot plot
• Tutorial: Drawing a boxplot
• Tutorial: Drawing a line chart
• Tutorial: Drawing a scatterplot

machine learning

• Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates

random forests

• Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates

R tip

• R tip: Ordering factor levels more easily

bootstrapping

• Interactions in logistic regression models
• Some illustrations of bootstrapping

replication

• Suggestions for more informative replication studies
• Draft: Replication success as predictive utility

predictive modelling

• Interpreting regression models: a reading list
• Draft: Replication success as predictive utility

assumptions

• Nonparametric tests aren't a silver bullet when parametric assumptions are violated
• Collinearity isn't a disease that needs curing
• Before worrying about model assumptions, think about model relevance
• Looking for comments on a paper on model assumptions
• Checking model assumptions without getting paranoid

research questions

• In research, don't do things you don't see the point of
• Interpreting regression models: a reading list
• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• A brief comment on research questions

Bayesian statistics

• Tutorial: Visualising statistical uncertainty using model-based graphs
• Baby steps in Bayes: Incorporating reliability estimates in regression models
• Baby steps in Bayes: Accounting for measurement error on a control variable
• Interactions in logistic regression models
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
• Baby steps in Bayes: Piecewise regression with two breakpoints
• Baby steps in Bayes: Piecewise regression

data entry

• A data entry form with sanity checks

preprint

• Capitalising on covariates in cluster-randomised experiments
• Looking for comments on a paper on model assumptions

cannonball

• Introducing cannonball - Tools for teaching statistics
• Looking for comments on a paper on model assumptions

brms

• Tutorial: Visualising statistical uncertainty using model-based graphs
• Interactions in logistic regression models
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons

contrast coding

• Interpreting regression models: a reading list
• Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
• Baby steps in Bayes: Recoding predictors and homing in on specific comparisons

walkthrough

• Walkthrough: A significance test for a two-group comparison

collinearity

• Collinearity isn't a disease that needs curing

Stan

• Baby steps in Bayes: Incorporating reliability estimates in regression models
• Baby steps in Bayes: Accounting for measurement error on a control variable

reliability

• Interpreting regression models: a reading list
• Baby steps in Bayes: Incorporating reliability estimates in regression models

nonparametric tests

• Nonparametric tests aren't a silver bullet when parametric assumptions are violated

research design

• Quantitative methodology: An introduction

teaching materials

• Quantitative methodology: An introduction

DAG

• The consequences of controlling for a post-treatment variable